Forecasting after adding dummy variable Using Tableau with R

Question

I am able to forecast using Forecast() function in tableau. I have daily hourly data(demand electricity). Previously i was using following code:

SCRIPT_REAL("library(forecast);
    jjearnts <- msts(.arg1, seasonal.periods=c(24, 7*24, 365*24));
    fit <- tbats(jjearnts);
    fcast <- forecast(fit,h=.arg2[1]);
    n<-length(.arg1);
    append(.arg1[(.arg2[1]+1):n],fcast$mean, after = n-.arg2[1])",
    SUM([ Load demand(electricity)]),[ Parameter])

The above code works but it lacks some dummy predictors. So i tried following code:

SCRIPT_REAL("library(forecast);
    modelfitsample <- data.frame(.arg1,Weekday=rep(1:7,7);
    xreg <- cbind(Weekday=model.matrix(~as.factor(modelfitsample$Weekday));
    xreg <- xreg[,-1];
    colnames(xreg) <- c("Mon","Tue","Wed","Thu","Fri","Sat");
    jjearnts <-ts(modelfitsample$.arg1,frequency=24*365,start=c(2008,90));
    fcast <- forecast(jjearnts, h=.arg2[1]);
    n<-length(.arg1);
    append(.arg1[(.arg2[1]+1):n],fcast$mean, after = n-.arg2[1])",
    SUM([Load demand(electricity)]),[ Parameter])

This code is not working